Review and Revision of Library and Information Science Curriculum in a South African University and the Usage of Follow-Up Study and Advertisement Scanning Methods
Bibliographic record
Abstract
Two methods for curriculum review and revision are used to review and revise the Library and Information (LIS) curriculum at the University of Zululand, South Africa. Firstly, as an exercise in product analysis, a case study of the graduates of the University of Zululand between 1996 and 1999 was conducted. Graduates were traced to their current places of employment and interviewed together with their employers in order to determine whether the knowledge, skills and attitudes gained during training were adequate for their current job requirements. Secondly, a market-type analysis was conducted by scanning job advertisements in the field of library and information science appearing in a popular national weekly newspaper over a period of three years. Details regarding date and location of advertisement, type of employer, job details and job specifications and requirements in terms of qualifications, experience, knowledge, skills and attitudes were captured from this source and analysed. Whereas the aforementioned two methods still enjoy popularity, arguably, they alone do not necessarily provide an accurate picture of the demand and supply matrix that can enhance effective and beneficial LIS education for service and employability of graduates. Evidently, the public sector and in particular the public and academic libraries, dominate this specific segment of the employment market in South Africa. Sound education in the fields of management, information and communication technologies, information searching, analysis and synthesis, as well as the ability to perform practical work is regarded as essential. The use of the aforementioned two methods exploits techniques which play a crucial verification role and which effectively supplement other methods such as reviewing existing curriculum and literature, consulting with colleagues and observing national and international trends as well as the focus-group method for academic programme development. Other intervening variables in the study are discussed. The paper addresses issues that can benefit theoretical and methodological issues in library and information science education and curriculum development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.025 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".